{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pretreat(csv):\n",
    "    \"\"\"\n",
    "    读取csv文件并进行预处理\n",
    "    :param csv: csv表格名称\n",
    "    :return: dataframe对象, 期货品种列表\n",
    "    \"\"\"\n",
    "    df = pd.read_csv('C:/Users/袁/Desktop/期末作业/' + csv + '.csv')\n",
    "\n",
    "    # 得到target中期货种类\n",
    "    names = df.columns[2:]\n",
    "\n",
    "    path = 'C:/Users/袁/Desktop/期末作业/1m'\n",
    "    files = os.listdir(path)\n",
    "    for file in files:\n",
    "        # 读取target表中所有期货的价格并合并到df中\n",
    "        if file[:-3] in names:\n",
    "            ft = pd.read_feather(path + '/' + file, columns=['trading_day','timestamp','clz'])\n",
    "            df = pd.merge(df,ft,on=['trading_day','timestamp'],how='left')\n",
    "            df = df.rename(columns={'clz':file[:-3]+'_clz'})\n",
    "\n",
    "    for name in names:\n",
    "        # 将市值乘以100w\n",
    "        df[name] = df[name] * 1000000\n",
    "        # 将仓位NaN数据替换为0\n",
    "        df[name].fillna(0, inplace=True)\n",
    "        # 将clz中部分NaN填充为上一交易日价格\n",
    "        df[name+'_clz'].fillna(method='ffill', inplace=True)\n",
    "        # 将仓位不为0但价格为0的数据视为异常，把仓位改为0\n",
    "    for name in names:\n",
    "        df.loc[(df[name]!=0) & (df[name+'_clz']!=df[name+'_clz']),name] = 0\n",
    "\n",
    "    return df, names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computer(df, names):\n",
    "    \"\"\"\n",
    "    计算dataframe各指标\n",
    "    :param df: dataframe对象\n",
    "    :param names: 期货品种列表\n",
    "    :return: dataframe对象\n",
    "    \"\"\"\n",
    "    for name in names:\n",
    "        # 计算期货份额\n",
    "        df[name+'_num'] = df[name] / df[name+'_clz']\n",
    "        df[name+'_num'].fillna(0, inplace=True)\n",
    "        # 计算现金流变化\n",
    "        df[name+'_cash_add'] = -(df[name+'_num'] - df[name+'_num'].shift(periods=1, axis=0)) * df[name+'_clz']\n",
    "        df[name+'_cash_add'].fillna(0, inplace=True)\n",
    "        # 计算交易费用\n",
    "        df[name+'_fee'] = abs(df[name+'_cash_add'] * 3/10000)\n",
    "        df[name+'_fee'].fillna(0, inplace=True)\n",
    "        # 计算turnover\n",
    "        df[name+'_turnover'] = -df[name+'_cash_add']\n",
    "\n",
    "    df['all'] = 0\n",
    "    df['cash_add'] = 0\n",
    "    df['fee'] = 0\n",
    "    df['turnover'] = 0\n",
    "    for name in names:\n",
    "        # 计算总市值\n",
    "        df['all'] += df[name]\n",
    "        # 计算总现金流变化\n",
    "        df['cash_add'] += df[name+'_cash_add']\n",
    "        # 计算总交易费用\n",
    "        df['fee'] += df[name+'_fee']\n",
    "        # 计算总turnover\n",
    "        df['turnover'] += df[name+'_turnover']\n",
    "\n",
    "    # 计算总现金及净现金\n",
    "    cash0 = 0\n",
    "    cash_lt = []\n",
    "    cash = cash0\n",
    "    cash_lt.append(cash)\n",
    "    for cash_add in df['cash_add'][1:]:\n",
    "        cash += cash_add\n",
    "        cash_lt.append(cash)\n",
    "    df['cash'] = cash_lt\n",
    "    df['netcash'] = df['cash'] - df['fee']\n",
    "\n",
    "    # 计算pnl\n",
    "    df['pnl'] = df['all'] + df['cash']\n",
    "    df['netpnl'] = df['all'] + df['netcash']\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_picture(df):\n",
    "    \"\"\"\n",
    "    打印折线图\n",
    "    :param df: dataframe对象\n",
    "    :print: 折线图\n",
    "    \"\"\"\n",
    "    # 将trading_day改为日期格式\n",
    "    df['trading_day'] = pd.to_datetime(df['trading_day'],format='%Y%m%d')\n",
    "    # 计算三个指标\n",
    "    SharpRatio = df['netpnl'].mean() / (df['netpnl'].std() * 15.8) *np.sqrt(240)\n",
    "    TurnOver = df['turnover'].mean() / 1000000\n",
    "    AvgLeverage = df['all'].mean() / 1000000\n",
    "    # 画图\n",
    "    plt.figure(figsize=(18,9))\n",
    "    plt.plot(df['trading_day'], df['pnl']/1000000, 'b', label='pnl_net')\n",
    "    plt.plot(df['trading_day'], df['netpnl']/1000000, 'r', label='pnl_net_after_limit')\n",
    "    title = 'SharpRatio: ' + str(round(SharpRatio,4)) + ', TurnOver: ' + str(round(TurnOver,4)) + ', AvgLeverage: ' + str(round(AvgLeverage,4))\n",
    "    plt.title(title)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x900 with 1 Axes>",
      "image/png": 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nTZtmSDIOHDhgGXPx4kXDxcXFePrppy19PXv2NKpWrWq1xYphGMaYMWMMV1dX4+LFi4ZhXP6d3alTp9zfuH/l9nu3IO9Ffl+nYRhG7dq1jdq1a1+zrtL6vBtG1nZH3t7exqeffmrpUy5beXz44YdGxYoVLVsGZf/+unorjyuxlQcAALljxTQAAGWEn5+f/vzzT23atElvvvmm+vXrp4MHD2r8+PFq0qSJzp8/bzW+WbNmql69uqXt6uqqevXqWf0Ju5T1cLIWLVrI1dVVjo6OcnJy0sqVK3Pc2uL222+3rFS92n333SeTyWRph4SEqH379jluVRAYGCgnJydVrFhRd999t1q2bKkvv/zSZlxBasuPdevWKSkpyWYVdLVq1dStWzetXLnSqv709PQc/4S+uC1dulSDBw/WgAEDtGTJEq1YsUIPPfSQHnjggRxXSmb75ptvlJycrIceesjm3GOPPaaDBw9qzJgxOnPmjE6dOqVRo0ZZvv/Zf0retGlTderUSe+8845++uknRUdH659//tGoUaNkNpvLzJ+cDxw4MMf+wMBAtWnTxqovLCzM5nN+LYZhSJLlM9yrVy8FBgZavf/Lli1TeHi4hg0bZulbtGiRunbtquDgYKWnp1u+evXqJUlas2aN1X1y+xlKT0+3+vzl5sp7pKenW+q+svac5HWuMNcX9V55MQxDw4cP159//qmvvvpK1apVK/Rc2T+/2d+z7L+COHHihOX99vPzU9++fa1WyF+6dEm//vqrhg4dallVu2bNGnl6eurWW2+1use9995b4LoOHz6s/fv3W1beX/k97d27tyIiInTgwAFJ0qBBg+Ti4mK1uvi7775TSkqKZTV3cnKyVq5cqf79+8vd3d1mvuTkZK1fv96qhtx+pvLzeze/70VBXmf2+Pw8XFAqvc/7qFGj1LRp0xz/AuJKJ06c0Pjx4/XOO+/kuGUQAAAouLLx/0QAAIBFq1at9Oyzz+qnn35SeHi4nnzySR0/flxvv/221Tg/Pz+ba11cXJSUlGRpv/fee3rkkUd00003ae7cuVq/fr02bdqkW2+91WpctqCgoFzruvpPqrP7Lly4YNP/+++/a9OmTVq2bJkGDhyotWvX6rHHHrMaU9Da8iO7lpxeR3BwcI61ljTDMDRs2DB16tRJX3zxhW699VbdcsstmjZtmu677z499thjSkhIyPHamTNnqnLlyurXr5/NuWHDhunNN9/U119/rapVq6p69erau3evZWuQKlWqWMb+9NNP6tChg+6++25VrFhRXbt21YABA9SsWTOrcfaU22cvP5/z6tWr69y5c7m+j5Is+5Bnh6COjo4aMmSIfvnlF0VHR0vK2johKChIPXv2tFx39uxZLVy4UE5OTlZf2dtPXP0PRnn9DF3L8ePHbe6THXz7+fnl+PlNSEhQamqqfH1985w7t+uzt3bIvj77/c5trMlkko+PT4Fe15WMf7dCmDNnjmbPnp3jZzu/4uLi9NNPP6lNmzaqXLmyoqOjFR0drf79+9vs2zxs2DCdOXNGK1askHQ5+L3yH7EuXLiQY+BYmBAyez/9cePG2XxPH330UUmXPzu+vr66/fbb9dVXXykjI0NS1mexTZs2ls/ZhQsXlJ6erg8//NBmvuytPvLzWczv7938vhcFeZ0FUVqf959//llLly7V22+/rZiYGMtnSMraBik6Otqyrdbo0aPVuHFjDRw40DIuMTFRkhQfH6+YmJgCv04AAMo726crAQCAMsPJyUkTJkzQ1KlTr7mnZk7mzJmjLl266JNPPrHqj4uLy3F8XivMIiMjc+zLKThs2rSpKlWqJEnq0aOHevbsqc8++0zDhw9X69atC1VbfmTXkv3gvyuFh4dbaipNZ8+eVUREhEaOHGlzrnXr1vrqq690/Phxm312t23bpm3btunpp5/OdRX7s88+qyeeeEKHDh2Sp6enQkJCNHLkSFWoUEEtW7a0jPP399fixYsVFRWlyMhIhYSEyM3NTf/73/905513Fu8L/peLi4tSUlJs+nP7x4GirMLt0aOHli9froULF+r//u//bM4bhqEFCxbI19fX6n158MEH9c477+j777/XPffcowULFuiJJ56Q2Wy2jKlUqZLCwsI0adKkHO8dHBxcbK8jODhYmzZtsuqrX7++JKlJkyb6/vvvFRkZafWPRNl7iTdu3DjPuZs0aaLvvvtO6enpVvvuXn197dq15ebmZum/0q5du1SnTp1rPrAzN9mh9KxZszRz5kwNHjy4UPNk++6775SYmKiNGzeqYsWKNud/+eUXXbp0SRUrVlTPnj0VHBysWbNmqWfPnpo1a5ZuuukmNWzY0DLez89PGzdutJknp99915L9u2b8+PFWD1e8Uvb3Vsr6LP70009asWKFqlevrk2bNln9bqxYsaLMZrOGDBmi0aNH5zhfzZo1rdo5fRbz+3s3v+9FQV9nfpXW53337t1KT09X27Ztbeb4/PPP9fnnn+uXX37RHXfcod27d+vEiRM5fta6du0qb29vS6gNAADyhxXTAACUETmFqZIsf159dQCWHyaTyeahdzt37tS6desKPNd3331nta3AiRMn9M8//1zzgW4mk0kff/yxzGazXnzxxQLXlj0mP6uo27VrJzc3N6sH40nS6dOn9ccff6h79+7XnKO4VaxYUa6urjZ/Zi9lbT3i4OCQ48rG7NWew4cPz3N+FxcXNW7cWCEhITp58qR++OEHjRgxQm5ubjZj/f39FRYWJm9vb02fPl0JCQkaM2ZMIV9Z3mrUqGHzYMU//vhD8fHxxX6vhx56SP7+/ho/fryioqJszr/99tvav3+/nnnmGauQv0GDBrrppps0a9Ysffvtt1ZbJ2Tr06ePdu/erdq1a6tVq1Y2X4X5ucyNs7Ozzfyenp6SpH79+slkMtlsiTN79my5ubnZbLlwtf79+ys+Pl5z58616v/yyy8VHBysm266SVLWSvK+fftq3rx5VmHlyZMntWrVqlzDx2sxDEMjRozQrFmz9Omnn9q8z4Uxc+ZMeXp6auXKlVq1apXV1zvvvKOUlBR98803kmQJdefPn68///xTmzdvttqyRZI6d+6suLg4LVmyxKr/+++/L3Bt9evXV926dbVjx44cPzdXfm8l6T//+Y+qVKmiWbNmadasWXJ1dbXaNsPd3V1du3bVtm3bFBYWluN8Of0j4dXy+3s3v+9FQV9nfpXW5/2BBx6w+exkb091xx13aNWqVbr55pstr/3qcc8++6ykrO1RFi1aVODXCQBAeceKaQAAyoiePXuqatWq6tu3r0JDQ5WZmant27drypQp8vDw0OOPP17gOfv06aPXXntNEyZMUOfOnXXgwAG9+uqrqlmzptLT0ws0V1RUlPr3768RI0YoJiZGEyZMkKurq8aPH3/Na+vWrauHH35Y//vf//TXX3/p5ptvzndt2SuBf/31V3Xv3l2+vr6qVKmSatSoYXMfHx8fvfTSS3r++ec1dOhQ3Xvvvbpw4YImTpwoV1dXTZgwwTL2xIkTql27tu6///587TP9888/S5KOHj0qSdq8ebM8PDwkyWrVcffu3bVmzRrLa3BxcdGjjz6q9957T0OHDtU999wjs9ms+fPn69tvv9Xw4cNt/iw9OTlZ3377rdq3b68GDRrkWM/u3bs1d+5ctWrVSi4uLtqxY4fefPNN1a1bV6+99prV2M8//1xS1mrY6OhoLVmyRDNnztQbb7yhFi1aWI3t0qWL1qxZY/WPEIUxZMgQvfTSS3r55ZfVuXNn7d27Vx999JG8vb2LNG9OfHx8NG/ePPXp00ctW7bUf//7XzVt2lSxsbH64Ycf9M033+iee+7Rf//7X5trhw0bppEjRyo8PFzt27e3Wd356quvasWKFWrfvr3Gjh2r+vXrKzk5WcePH9fixYs1ffp0Va1a9Zo1Ojo6qnPnzvnaZzonjRo10vDhwzVhwgSZzWa1bt1ay5cv12effabXX3/d6jP06quv6tVXX9XKlSvVuXNnSVl7avfo0UOPPPKIYmNjVadOHX333XdaunSp5syZY7VKfOLEiWrdurX69Omj5557TsnJyXr55ZdVqVIlPf3001Z1bd682bJNSmxsrAzDsPystG7dWiEhIZKksWPHaubMmRo2bJiaNGli9Q81Li4uat68uaX9yiuvaOLEiVq1alWu//C1e/dubdy4UY888oi6detmc75Dhw6aMmWKZs6cafnHl2HDhumtt97SfffdJzc3N91zzz1W19x///2aOnWqBg8erNdff1116tTRkiVLtGzZMknKcT/2hQsX5hi83nnnnfr000/Vq1cv9ezZUw888ICqVKmiixcvat++fdq6dat++ukny3iz2ayhQ4fqvffek5eXlwYMGGDzs/LBBx/o5ptvVseOHfXII4+oRo0aiouL0+HDh7Vw4UL98ccfOb5XV8rv792CvBcFeZ116tSRpGvuM11an/caNWrk+N8SKWs7pCs/fzmtqs7+7Lds2VKtWrWyOpff/2YAAFCu2emhiwAA4Co//PCDcd999xl169Y1PDw8DCcnJ6N69erGkCFDjL1791qNDQkJMW677TabOTp37mx07tzZ0k5JSTHGjRtnVKlSxXB1dTVatGhhzJ8/37j//vuNkJAQy7hjx44Zkox33nnHZs5Vq1YZkoyvv/7aGDt2rFG5cmXDxcXF6Nixo7F582arsRMmTDAkGefOnbOZ5+zZs4aHh4fRtWvXAtVmGIbx+++/G82bNzdcXFwMScb9999vGIZhzJo1y5BkHDt2zGr8jBkzjLCwMMPZ2dnw9vY2+vXrZ+zZs8dqTPZrzp7rWiTl+nWlzp072/RlZGQYn3/+udGqVSvDx8fH8PLyMpo3b2589NFHRmpqqs29vvnmG0OS8cUXX+Raz4EDB4xOnToZvr6+hrOzs1GnTh3jxRdfNOLj423Gfvrpp0aDBg0Md3d3w8PDw+jYsaMxf/78HOdt2bKlERgYmJ+3xOo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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    data, names = pretreat('SampleTarget')\n",
    "    print_picture(computer(data, names))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
